Method for adjusting yaw angle of wind turbine in real time
By optimizing the yaw angle of wind turbines using mesoscale meteorological models and an improved particle swarm search algorithm, the problem of yaw angle calculation error for wind turbine units was solved, resulting in more efficient wind energy utilization and reduced power generation costs.
Patent Information
- Application Number
- CN202511436381.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-09
- Publication Date
- 2025-12-30
AI Technical Summary
In existing technologies, the calculation of yaw angle for wind turbines has accumulated errors, resulting in inaccurate yaw control and affecting wind energy utilization and power generation costs.
By collecting parameter information of the target wind farm, meteorological data simulation is carried out using a mesoscale meteorological model, and the yaw angle of the wind turbine is optimized by combining an improved particle swarm search algorithm. The yaw angle is further optimized by using a wake model and a dynamic weight update mechanism.
It improves the accuracy, real-time performance, and adaptability of wind turbine yaw angle adjustment, and optimizes the power generation efficiency of wind farms.
Smart Images
Figure CN121229329A_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of power generation technology, and in particular to a method for real-time adjustment of the yaw angle of a wind turbine. Background Technology
[0002] With the depletion of fossil fuels and increasing environmental pressure, many scholars and engineers have begun to develop and utilize new energy sources, such as wind power. In yaw control, calculating the yaw angle of a wind turbine is crucial. Currently, the yaw angle of a wind turbine is typically calculated by the main control system based on pulse signals whose frequency is linearly related to the current yaw speed, collected by the yaw position sensor. While this method is simple and practical, long-term calculations can lead to accumulated errors, resulting in inaccurate yaw angle calculations. This, in turn, affects the yaw control of the wind turbine, reduces wind energy utilization, and increases power generation costs.
[0003] It is evident that there is an urgent need for a method for real-time adjustment of wind turbine yaw angle with high accuracy, real-time performance, and adaptability. Summary of the Invention
[0004] In view of this, the present disclosure provides a method for real-time adjustment of the yaw angle of a wind turbine, which at least partially solves the problems of poor adjustment accuracy, real-time performance and adaptability in the prior art.
[0005] This disclosure provides a method for real-time adjustment of the yaw angle of a wind turbine, including:
[0006] Step 1: Collect parameter information of the target wind farm, and use a mesoscale meteorological model to simulate the meteorological data of the target wind farm to obtain simulation results;
[0007] Step 2: Using the simulation results as the optimization background, the improved particle swarm search algorithm is used to optimize the yaw angle of each wind turbine in the target wind farm.
[0008] According to a specific implementation of this disclosure, the parameter information includes the latitude and longitude coordinates of the target wind farm site, the relative coordinates of each wind turbine, the wind turbine model, and power curve data.
[0009] The simulation results include the wind speed and direction of the target wind farm at the next moment.
[0010] According to a specific implementation of an embodiment of this disclosure, step 2 specifically includes:
[0011] Step 2.1: Initialize the initial position and initial velocity of the particle swarm. Define the dimension of each individual particle in the swarm as the number of wind turbines, and the value corresponding to each dimension is the yaw angle of the corresponding wind turbine.
[0012] Step 2.2: Based on the wind speed and direction of the target wind farm at the next moment, divide the target wind farm into grids and discretize them. Use the wake model to calculate the average flow field of each grid point in the target wind farm under the current wind environment and the dimensional information of a certain individual. Then, calculate the power generation of all wind turbines as fitness based on the average flow field.
[0013] Step 2.3: Update the velocity and position of each individual based on fitness;
[0014] Step 2.4: Repeat the fitness calculation, velocity update, and position update process until the maximum number of iterations is reached. Then, output the individual with the best fitness among all individuals as the yaw angle of each wind turbine in the next moment of wind speed and direction environment predicted by the mesoscale meteorological model.
[0015] According to a specific implementation of this disclosure, the expression for the wake model is:
[0016]
[0017] in, Let (x, y, z) be the wind speed value corresponding to the grid point (x, y, z). For the inflow velocity of the fan, For reference height, The axial induction factor for wind turbines. , This is the thrust coefficient of the wind turbine. This represents the total number of wind turbines. The wind speed loss is for the i-th wind turbine;
[0018] The expression for fitness is:
[0019]
[0020] in, Let be the average inflow wind speed of the i-th wind turbine. This is a function for calculating the power of a single wind turbine. , To achieve the wind speed at maximum power, The standard deviation of the wind turbine power curve. for ;
[0021] , For the area of the wind turbine, This represents the integral over the area.
[0022] According to a specific implementation of this disclosure, the expression for the speed update is:
[0023]
[0024] in, It is the velocity of the i-th particle in the t-th iteration. It is the position of the i-th particle in the t-th iteration. It is the best position found so far by the i-th particle. It is the globally optimal position found by all particles so far. and These are acceleration coefficients, representing the weights of the particle's movement towards its own optimal position and the global optimal position, respectively. and It is a random number between 0 and 1, and w(t) is a dynamic weight that is related to the fitness.
[0025]
[0026] Where w(t) is the dynamic weight at the t-th iteration, with an initial value of 1. These are the minimum and maximum values of the inertia weight. It is the maximum fitness value in the current particle swarm. It is the minimum fitness value in the current particle swarm. It is a constant value. It is the gradient of the inflow velocity of all wind turbines in the flow field, and its expression is:
[0027] ;
[0028] The expression for the location update is:
[0029] .
[0030] The real-time yaw angle adjustment scheme for wind turbines in this embodiment includes: Step 1, collecting parameter information of the target wind farm, and using a mesoscale meteorological model to simulate meteorological data of the target wind farm to obtain simulation results; Step 2, using the simulation results as the optimization background, and using an improved particle swarm search algorithm to optimize the yaw angle of each wind turbine in the target wind farm.
[0031] The beneficial effects of the embodiments of this disclosure are as follows: Through the scheme of this disclosure, the wind characteristics such as incoming wind speed and wind direction at the wind farm site are predicted in real time by using the mesoscale model WRF, and the optimal yaw angle matrix of the wind turbines at fixed locations in the wind farm is optimized based on the wind turbine wake model that can take yaw into account and the improved particle swarm algorithm, thereby improving the adjustment accuracy, real-time performance and adaptability. Attached Figure Description
[0032] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0033] Figure 1 A flowchart illustrating a method for real-time adjustment of the yaw angle of a wind turbine provided in this embodiment of the present disclosure;
[0034] Figure 2 This is a schematic diagram illustrating the specific implementation process of a method for real-time adjustment of the yaw angle of a wind turbine, as provided in this embodiment of the disclosure. Detailed Implementation
[0035] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.
[0036] The following specific examples illustrate the implementation of this disclosure. Those skilled in the art can easily understand other advantages and effects of this disclosure from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. This disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this disclosure. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.
[0037] It should be noted that various aspects of embodiments within the scope of the appended claims are described below. It will be apparent that the aspects described herein can be embodied in a wide variety of forms, and any particular structure and / or function described herein is merely illustrative. Based on this disclosure, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement the device and / or practice the method. Additionally, this device and / or method can be implemented using structures and / or functionalities other than one or more of the aspects set forth herein.
[0038] It should also be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this disclosure. The drawings only show the components related to this disclosure and are not drawn according to the number, shape and size of the components in actual implementation. In actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.
[0039] Furthermore, specific details are provided in the following description to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the described aspects can be practiced without these specific details.
[0040] This disclosure provides a method for real-time adjustment of the yaw angle of a wind turbine, which can be applied to the wind turbine control process in power generation scenarios.
[0041] See Figure 1 This is a flowchart illustrating a method for real-time adjustment of the yaw angle of a wind turbine provided in an embodiment of this disclosure. Figure 1 and Figure 2 As shown, the method mainly includes the following steps:
[0042] Step 1: Collect parameter information of the target wind farm, and use a mesoscale meteorological model to simulate the meteorological data of the target wind farm to obtain simulation results;
[0043] Optionally, the parameter information includes the latitude and longitude coordinates of the target wind farm site, the relative coordinates of each wind turbine, the wind turbine model, and power curve data;
[0044] The simulation results include the wind speed and direction of the target wind farm at the next moment.
[0045] In practice, the latitude and longitude coordinates of the target wind farm site, the relative coordinates of each wind turbine, the wind turbine model and power curve data can be collected first. Then, the mesoscale meteorological model WRF can be used to simulate the meteorological data of the target site in real time, and predict the wind speed and wind direction of the wind farm in the next hour in advance, so as to carry out subsequent operation procedures based on the prediction results.
[0046] Step 2: Using the simulation results as the optimization background, the improved particle swarm search algorithm is used to optimize the yaw angle of each wind turbine in the target wind farm.
[0047] Based on the above embodiments, step 2 specifically includes:
[0048] Step 2.1: Initialize the initial position and initial velocity of the particle swarm. Define the dimension of each individual particle in the swarm as the number of wind turbines, and the value corresponding to each dimension is the yaw angle of the corresponding wind turbine.
[0049] Step 2.2: Based on the wind speed and direction of the target wind farm at the next moment, divide the target wind farm into grids and discretize them. Use the wake model to calculate the average flow field of each grid point in the target wind farm under the current wind environment and the dimensional information of a certain individual. Then, calculate the power generation of all wind turbines as fitness based on the average flow field.
[0050] Step 2.3: Update the velocity and position of each individual based on fitness;
[0051] Step 2.4: Repeat the fitness calculation, velocity update, and position update process until the maximum number of iterations is reached. Then, output the individual with the best fitness among all individuals as the yaw angle of each wind turbine in the next moment of wind speed and direction environment predicted by the mesoscale meteorological model.
[0052] Furthermore, the expression for the wake model is as follows:
[0053]
[0054] in, Let (x, y, z) be the wind speed value corresponding to the grid point (x, y, z). For the inflow velocity of the fan, For reference height, The axial induction factor for wind turbines. , This is the thrust coefficient of the wind turbine. This represents the total number of wind turbines. The wind speed loss is for the i-th wind turbine;
[0055] The expression for fitness is:
[0056]
[0057] in, Let be the average inflow wind speed of the i-th wind turbine. This is a function for calculating the power of a single wind turbine. , To achieve the wind speed at maximum power, The standard deviation of the wind turbine power curve. for ;
[0058] , For the area of the wind turbine, This represents the integral over the area.
[0059] Furthermore, the expression for the speed update is:
[0060]
[0061] in, It is the velocity of the i-th particle in the t-th iteration. It is the position of the i-th particle in the t-th iteration. It is the best position found so far by the i-th particle. It is the globally optimal position found by all particles so far. and These are acceleration coefficients, representing the weights of the particle's movement towards its own optimal position and the global optimal position, respectively. and It is a random number between 0 and 1, and w(t) is a dynamic weight that is related to the fitness.
[0062]
[0063] Where w(t) is the dynamic weight at the t-th iteration, with an initial value of 1. These are the minimum and maximum values of the inertia weight. It is the maximum fitness value in the current particle swarm. It is the minimum fitness value in the current particle swarm. It is a constant value. It is the gradient of the inflow velocity of all wind turbines in the flow field, and its expression is:
[0064] ;
[0065] The expression for the location update is:
[0066] .
[0067] In practice, the real-time yaw angle prediction uses the simulation results of WRF as the optimization background, and the improved particle swarm search algorithm is used to optimize the yaw angle of each wind turbine. The specific steps are as follows:
[0068] 2.1 Initialization
[0069] 1. The particle swarm optimization dimension is the number of wind turbines N, and the range of each dimension is divided into M equal intervals (M is the number of particles). The length of each interval is:
[0070]
[0071] In the formula, ub represents the upper bound of the optimization, and lb represents the lower bound. In the yaw angle optimization problem, ub is 2π and lb is 0. For each dimension, a random sequence is generated. Then the initial position of the j-th particle in the i-th dimension is:
[0072]
[0073] In the formula, u is a random number in the range [0, 1]. The velocity is initialized using a random offset from the initial position of the particle. The initial velocity of the j-th particle in the i-th dimension is:
[0074]
[0075] In the formula, This is the proportionality coefficient. To initialize random values within the location range.
[0076] 2.2 Fitness Calculation
[0077] Subsequently, the fitness of individuals within the population is calculated, which is the sum of the power generation of all wind turbines in the wind farm. The calculation method is as follows: The ambient wind speed and direction at the wind farm location are extracted using WRF; the wind farm is then divided into grids and discretized; the wake model is used to calculate the average flow field at each grid point within the wind farm under the current wind environment and the yaw condition of the wind turbines (i.e., the dimensional information of a particular individual); and finally, the power generation of all wind turbines is calculated based on the average flow field. The expression for the wind turbine wake model is:
[0078]
[0079] in, Let (x, y, z) be the wind speed value corresponding to the grid point (x, y, z). For the inflow velocity of the fan, For reference height, The axial induction factor for wind turbines. , This is the thrust coefficient of the wind turbine. This represents the total number of wind turbines. The wind speed loss is for the i-th wind turbine;
[0080]
[0081] Where p is the coordinate of the grid point. The center offset of the wake influence area:
[0082]
[0083] In the formula, o is the coordinate of the grid point. Yaw angle The wake development coefficient, The radius of influence of the wake in the q-axis direction. It is a power exponent. Mass flux downstream of the wind turbine:
[0084]
[0085] For Jensen's wake radius growth rate, For the installation height of the wind turbine, For the radius of the wind turbine, To account for the wind speed difference of the shear inflow, .
[0086]
[0087] In the above formula: The longitudinal (i.e., p-axis direction) wake radius linear expansion rate, The vertical (i.e., q-axis direction) wake radius linear expansion rate, This is the thrust coefficient of the wind turbine. The turbulence intensity of the incoming airflow at the hub height.
[0088] Subsequently, the radius of influence of the wake was calculated based on the linear assumption:
[0089]
[0090] After the flow field information within the wind farm is calculated, the total power generation of the wind farm is calculated by combining the flow field calculation results with the wind turbine power curve. The calculation formula is as follows:
[0091]
[0092] In the formula, Let be the average inflow wind speed of the i-th wind turbine. This is a function for calculating the power of a single wind turbine. , To achieve the wind speed at maximum power, The standard deviation of the wind turbine power curve. for ; , For the area of the wind turbine, This represents the integral over the area. The calculated W is the fitness of that individual.
[0093] 2.3 Individual Renewal
[0094] Next, the velocity of each particle is calculated, and the velocity update formula is as follows:
[0095]
[0096] in, It is the velocity of the i-th particle in the t-th iteration. It is the position of the i-th particle in the t-th iteration. It is the best position found so far by the i-th particle. It is the best global position found by all particles so far. and These are acceleration coefficients, representing the weights of the particle's movement toward its own optimal position and the global optimal position, respectively. and It is a random number between 0 and 1. w(t) is the dynamic weight, which is related to the fitness, and is calculated as follows:
[0097]
[0098] In the formula, w(t) is the dynamic weight at the t-th iteration, with an initial value of 1. These are the minimum and maximum values of the inertia weight. It is the maximum fitness value in the current particle swarm. It is the minimum fitness value in the current particle swarm. It is a very small value used to avoid cases where the denominator is zero. It is the gradient of the inflow velocity of all wind turbines in the flow field, and its calculation formula is:
[0099]
[0100] The formula for calculating the position update of each particle is as follows:
[0101]
[0102] 2.4 Iterative Calculation
[0103] Subsequently, the fitness calculation, velocity update, and position update process is repeated until the maximum number of iterations is reached. Then, the individual with the best fitness among all individuals is output, which is the yaw angle that each wind turbine should yaw under the wind speed and direction environment predicted by WRF in the next moment.
[0104] The real-time yaw angle adjustment method for wind turbines provided in this embodiment uses the mesoscale model WRF to predict the wind characteristics such as incoming wind speed and direction at the wind farm site in real time. Based on the wake model of the wind turbine that can take yaw into account and the improved particle swarm algorithm, the optimal yaw angle matrix of the wind turbines at fixed locations in the wind farm is optimized, thereby improving the adjustment accuracy, real-time performance and adaptability.
[0105] It should be understood that the various parts of this disclosure can be implemented in hardware, software, firmware, or a combination thereof.
[0106] The above description is merely a specific embodiment of this disclosure, but the scope of protection of this disclosure is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this disclosure should be included within the scope of protection of this disclosure. Therefore, the scope of protection of this disclosure should be determined by the scope of the claims.
Claims
1. A method for real-time adjustment of yaw angle of a wind turbine, characterized in that, The application relates to a method for optimizing the yaw angle of a wind turbine in a target wind farm. The method comprises the following steps: Step 1: collecting parameter information of the target wind farm, and simulating meteorological data of the target wind farm by using a mesoscale meteorological model according to the parameter information to obtain a simulation result; 2. The method of claim 1, wherein, Step 2: taking the simulation result as an optimization background, and optimizing the yaw angle of each wind turbine in the target wind farm by using an improved particle swarm search algorithm. The parameter information comprises longitude and latitude coordinates of the target wind farm site, relative coordinates of each wind turbine, a wind turbine model and power curve data.
3. The method of claim 2, wherein, The simulation result comprises wind speed and wind direction of the target wind farm at the next moment. Step 2 specifically comprises the following steps: Step 2.1: initializing the initial position and initial speed of the particle swarm, and defining the dimension of each individual in the particle swarm as the number of wind turbines, and the value corresponding to each dimension as the yaw angle of the corresponding wind turbine; Step 2.2: dividing the target wind farm into grids and discretizing the target wind farm according to the wind speed and wind direction of the target wind farm at the next moment, calculating the average flow field of each grid point in the target wind farm under the current wind environment and the dimension information of an individual by using a wake model, and then calculating the power generation of all wind turbines based on the average flow field to obtain the fitness; Step 2.3: updating the speed and position of each individual according to the fitness; 4. The method of claim 3, wherein, Step 2.4: repeating the fitness calculation, speed updating and position updating until the maximum iteration number is reached, and then outputting the individual with the optimal fitness in the previous generations as the yaw angle of each wind turbine under the wind speed and wind direction environment predicted by the mesoscale meteorological model at the next moment. ； wherein, is the wind speed value corresponding to the grid point (x, y, z), is the wind turbine inflow speed, is the reference height, is the wind turbine axial induction factor, , is the wind turbine thrust coefficient, is the total number of wind turbines, is the wind speed deficit of the i-th wind turbine; The expression of the wake model is ； wherein, is the average wind speed of the inflow for the i-th wind turbine, is the single wind turbine power calculation function, , is the wind speed at which the maximum power is reached, is the standard deviation of the wind turbine power curve, is the ; , is the wind wheel area, denotes the area integral.
5. The method of claim 4, wherein, The expression of the fitness is ； wherein, is the velocity of the i-th particle at the t-th iteration, is the position of the i-th particle at the t-th iteration, is the best position found so far by the i-th particle, is the global best position found so far by all particles, and are acceleration coefficients, representing the weight of the movement of the particle towards its own best position and the global best position, respectively, and are random numbers between 0 and 1, w(t) is a dynamic weight, related to the fitness. ； where w(t) is the dynamic weight at the tth iteration, and its initial value is 1, is the minimum and maximum value of the inertia weight, is the maximum fitness value in the current particle swarm, is the minimum fitness value in the current particle swarm, is a constant value, is the variation gradient of the inflow velocity of all wind turbines in the flow field, and its expression is: ； The expression of the speed updating is The expression of the position updating is The application further discloses a computer readable storage medium storing the computer program. 。